Haixia Su
Impact in
-
- Computational Drug Discovery Methods
- Infectious Diseases top 5%
- SARS-CoV-2 and COVID-19 Research
- COVID-19 Clinical Research Studies
Papers in
-
- Peroxisome Proliferator-Activated Receptors 4
- Ubiquitin and proteasome pathways 3
-
- Computational Drug Discovery Methods 17
- Co-authors
- Yechun Xu (36 shared papers)Hang Xie (11 shared papers)Qiang Shao (9 shared papers)Wenfeng Zhao (6 shared papers)Muya Xiong (8 shared papers)Minjun Li (13 shared papers)Hualiang Jiang (4 shared papers)Leike Zhang (8 shared papers)
In The Last Decade
Haixia Su
56 papers receiving 981 citations
Peers
Comparison fields: 5 of 120
- Computational Theory and Mathematics 322
- Infectious Diseases 242
- Molecular Biology 432
- Pharmacology 53
- Organic Chemistry 177
Countries citing papers authored by Haixia Su
This map shows the geographic impact of Haixia Su's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Haixia Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haixia Su more than expected).
Fields of papers citing papers by Haixia Su
This network shows the impact of papers produced by Haixia Su. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Haixia Su. The network helps show where Haixia Su may publish in the future.
Co-authors
The 25 scholars most cited alongside Haixia Su, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 151 | |
| 2 | 2021 | 84 | |
| 3 | 2021 | 68 | |
| 4 | 2019 | 50 | |
| 5 | 2018 | 48 | |
| 6 | 2021 | 34 | |
| 7 | 2020 | 30 | |
| 8 | 2022 | 27 | |
| 9 | 2021 | 26 | |
| 10 | 2020 | 26 | |
| 11 | 2023 | 25 | |
| 12 | 2018 | 21 | |
| 13 | 2022 | 20 | |
| 14 | 2023 | 20 | |
| 15 | 2020 | 20 | |
| 16 | 2021 | 19 | |
| 17 | 2020 | 19 | |
| 18 | 2016 | 18 | |
| 19 | 2021 | 17 | |
| 20 | 2016 | 17 |
About Haixia Su
Haixia Su is a scholar working on Molecular Biology, Computational Theory and Mathematics, Infectious Diseases, Immunology and Control and Systems Engineering, having authored 58 papers that have together received 987 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (17 papers), SARS-CoV-2 and COVID-19 Research (13 papers), Fault Detection and Control Systems (6 papers), Peroxisome Proliferator-Activated Receptors (4 papers), Ubiquitin and proteasome pathways (3 papers), COVID-19 Clinical Research Studies (3 papers), Immune Response and Inflammation (3 papers) and Click Chemistry and Applications (3 papers). The work is most often cited by research in Computational Theory and Mathematics (322 citations), Infectious Diseases (242 citations), Molecular Biology (432 citations), Pharmacology (53 citations) and Organic Chemistry (177 citations). Haixia Su has collaborated with scholars based in China, Germany and Belgium. Frequent co-authors include Yechun Xu, Hang Xie, Qiang Shao, Wenfeng Zhao, Muya Xiong, Minjun Li, Hualiang Jiang, Leike Zhang, Guigang Zhang and Chang‐Qiang Ke. Their work appears in journals such as European Journal of Medicinal Chemistry, Journal of Medicinal Chemistry, Nature Communications, BioMed Research International and Molecular BioSystems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.